**ORCID iD**

**Research Interests**

- Computational Social Science
- Data Science
- E-Government
- Machine Learning
- Public Administration
- Public Policy

**Doctoral Research**

**Valdosta State University IRB 04125-2021 Protocol Exemption Approval**

**Quantitative Research – A Data Science and Machine Learning Approach to Comparative COVID-19 Policy Responses – (January 2021-Present)**

- Study aim is to employ Data Science and Machine Learning in the investigation of health and economic policy responses to the COVID-19 pandemic in developed countries and developing countries approved by Valdosta State University IRB.
- Prior research indicated more rigorous research is needed for contribution to the body of knowledge from a Public Policy perspective.
- Examining literature for the development of the research design and execution to include two-tailed (non-directional) research hypotheses, null hypothesis, independent variable(s), dependent variable(s), etc.
- Research aim is to answer how does Data Science and Machine Learning can inform Public Policy about the COVID-19 pandemic, what is the state of the COVID-19 pandemic in developed countries and developing countries, and empirically assess the correlation between policy responses and the state of COVID-19 in select countries.
- Employing secondary data from multiple data sources.
- Data wrangling, data analysis, and data visualization will be executed in RStudio.
- Initial data analysis will include time series analysis, text mining, sentiment analysis, and spatial data analysis.
- Creating R Scripts for reproducibility of data wrangling, data analysis, and data visualization in RStudio.

**Valdosta State University IRB 03979-2020 Protocol Exemption Approval**

**Quantitative Research – Measuring the Effectiveness of E-Government Delivery Models in Developed Countries and Developing Countries – (January 2020-Present)**

- Measuring the effectiveness of E-Government delivery models in developed countries and developing countries is the second phase of the quantitative study measuring the effectiveness of e-government delivery models approved by Valdosta State University IRB.
- Examined literature for the development of the research design and execution.
- Research aim is to answer what is the state of e-government delivery models in developed countries and developing countries.
- Employing secondary data from the first study phase plus developing two new datasets for data analysis.
- Integrating data science and machine learning algorithms for data analysis in R.
- Developed two-tailed research hypotheses (H
_{1}, H_{2}, H_{3}, H_{4, }H_{5}, H_{6}) and null hypothesis (H_{0}). - Creating R Scripts for reproducibility of data wrangling, data analysis, and data visualization in RStudio.
- Performing descriptive statistics with summary function and describe function.
- Creating boxplots to visually depict outliers with boxplot function and identified outliers with boxplot.stats function.
- Employing ANOVA with the aov function to identify significant differences and Tukey’s Honest Significant Differences with the TukeyHSD function to identify the location of significant differences.
- Employing K-means clustering with the kmeans function to cluster similar observations into groups and Hierarchal clustering with the hclust function to group observations based on similarities. Creating a dendrogram to depict the hierarchal relationships of the clusters as a tree diagram.

**Quantitative Research – Measuring the Effectiveness of E-Government Delivery Models from a Public Administration Perspective – (August 2019-Present)**

- Examined literature for the development of the research design and execution.
- Research aim is to answer what is the state of e-government delivery models globally.
- Employing a quantitative longitudinal design utilizing secondary data.
- Developed two-tailed research hypotheses (H
_{1}, H_{2}, H_{3}, H_{4, }H_{5}, H_{6}) and null hypothesis (H_{0}). - Imported datasets into RStudio with read_csv function.
- Loaded R packages: tidyverse, psych, stats, rmarkdown.
- Created tibbles with as_tibble function.
- Performed descriptive statistics with summary function and describe function.
- Performed Pearson correlation analysis with corr.test function, created correlation matrix with round(cor) function, and depicted correlation analysis on scatterplots with plot function.
- Created boxplots to visually depict outliers with boxplot function and identified outliers with boxplot.stats function.
- Created six simple linear regression models and two multiple linear regression models with lm function.
- Created regression diagnostic plots (residuals vs fitted, normal q-q, scale-location, residuals vs leverage) for each regression model with par function and plot function.
- Creating R Scripts for reproducibility of data wrangling, data analysis, and data visualization in RStudio.
- Preparing for scholarly publications and presentations.
- Citing R, RStudio, and R packages with citation function and RStudio.Version function for scholarly publications and presentations.

**Independent Research**

**Quantitative Research – A Public Administration Research Approach and Empirical Perspective of COVID-19 – (March 2020-present)**

- Examining literature for the development of the research design and execution.
- Research aim is to employ data science and machine learning algorithms in a public administration research approach and empirical perspective of COVID-19.
- Time series analysis will be employed in R to examine the economic impact of COVID-19 related policies.
- Text mining will be employed in R of COVID-19 research literature for text processing, modeling, analysis, and visualization.
- Naïve Bayes will be employed in R for text classification.
- Sentiment analysis will be employed in R to examine public opinion of COVID-19 related policies.
- Spatial analysis will be employed in R of COVID-19 longitude and latitude cases.

**Quantitative Research – Integration, Challenges, and Future Direction of Data Science in Public Administration – (August 2019-present)**

- Examining literature for the development of the research design and execution.
- Research aim is to answer what is the state of data science in public administration.
- Integrating data science and machine learning algorithms for data analysis in R.
- Loaded R packages: tidyverse, psych, stats, rmarkdown.
- Initial data collected and wrangling dataset A with 23,859 observations and dataset B with 16,716 observations in R
- Importing datasets with read_csv function in RStudio.
- Creating tibbles with as_tibble function.
- Skipping rows with skip = function.
- Deleting variables with select function.
- Combining columns with unite function.